Measurement and simulation of the relatively competitive advantages and weaknesses between economies based on bipartite graph theory

Abstract

The input-output table is very comprehensive and detailed in describing the national economic systems with abundant economic relationships, which contain supply and demand information among various industrial sectors. The complex network, a theory, and method for measuring the structure of a complex system can depict the structural characteristics of the internal structure of the researched object by measuring the structural indicators of the social and economic systems, revealing the complex relationships between the inner hierarchies and the external economic functions. In this paper, functions of industrial sectors on the global value chain are to be distinguished with bipartite graph theory, and inter-sector competitive relationships are to be extracted through resource allocation process. Furthermore, quantitative analysis indices will be proposed under the perspective of a complex network, which will be used to bring about simulations on the variation tendencies of economies’ status in different situations of commercial intercourses. Finally, a new econophysics analytical framework of international trade is to be established.

Nestedness-Based Measurement of Evolutionarily Stable Equilibrium of Global Production System

“…In spite of this being the same mechanism as the one-mode GIVCN model, a two-mode network is able to identify the underlying cooperative relationships between industrial sectors. That is, there is cooperation between upstream sectors to promote the production of their common downstream sectors [32]. The authors believe that the flow of intermediate goods in production systems (expressed in the IO table as value or currency flow) is similar to the flow of energy in ecosystems, and that both systems converge to a steady state after a complex game.…”

Nestedness-Based Measurement of Evolutionarily Stable Equilibrium of Global Production System

“…While the GIVCNBG model applies the weight set W to display the adjacency matrix, each row refers to the distribution of intermediate goods output from an upstream sector to several downstream sectors and each column the intermediate goods input obtained by a downstream sector from several upstream sectors. In spite of the same mechanism as the one-mode GIVCN model, a two-mode network is able to identify the underlying cooperative relationships between industrial sectors: cooperation between upstream sectors to promote the production of downstream sectors [20]. The authors believe that the flow of intermediate goods in production systems (expressed in the IO table as value flowing) is similar in nature to the flow of energy in ecosystems, and that both systems converge to a steady state after a complex game between subjects.…”

Latent Dirichlet Allocation Models for World Trade Analysis

“…Also, Curaçao (component 14) presents data only for 2011-2016 and Botswana (component 15) from 2000 (with 64.3% probability in "Diamonds (non-industrial), not mounted or set" exports and rising to 88.3% in 2016), albeit the country only exported 1.4% of that product globally in 2000 (although that share grew to 4.7% in 2016). 14 Further, only one component (22) does not show a particular regularity that can explain the representative country (Ghana): its main product ("Petroleum gases and other gaseous hydrocarbons, nes, liquefied", with a 38% probability) is currently mainly exported by Qatar, rising from 0.2% (in 1975) to 22.4% (in 2016). 15 Finally, another interesting fact derived from our LDA model is that there is one product ("Passenger motor vehicles (excluding buses)") captured as the main one in six of the 30 components (3, 6, 10, 11, 17 and 30).…”